Publications by Year: 2015

2015
G. Li, Y. Gu, and Y. M. Lu, “Phase Retrieval Using Iterative Projections: Dynamics in the Large Systems Limit,” in Allerton Conference on Communications, Control, and Computing, 2015.Abstract

We study phase retrieval by using a simple non-convex algorithm based on iterative projections. Our main contribution in this work is to establish an exact analysis of the dynamics of the algorithm in an online setting, with Gaussian measurement vectors. We show that the algorithm dynamics, measured by the squared distance between the current estimate and the true solution, can be fully characterized by a 2D Markov process, irrespective of the underlying signal dimension. Furthermore, in the large systems limit (i.e., as the signal dimension tends to infinity), the random sample paths of the 2D Markov process converge to the solutions of two deterministic and coupled ordinary differential equations (ODEs). Numerical simulations verify the accuracy of our analytical predictions, even for moderate system sizes. This suggests that the ODE approach presented in this work provides an effective tool for analyzing the performance and convergence of the algorithm.

phase_dynamics.pdf
S. H. Chan, T. Zickler, and Y. M. Lu, “Understanding symmetric smoothing filters via Gaussian mixtures,” in IEEE International Conference on Image Processing, 2015.
J. Onativia, P. L. Dragotti, and Y. M. Lu, “Sparsity according to Prony, Average Performance Analysis,” in Signal Processing with Adaptive Sparse Structured Representations (SPARS) Workshop, Cambridge, England, 2015. 2015_spars.pdf
C. Hu, et al., “A Spectral Graph Regression Model for Learning Brain Connectivity of Alzheimer's Disease,” PLOS ONE, vol. 10, no. 5, 2015. Publisher's VersionAbstract

Understanding network features of brain pathology is essential to reveal underpinnings of neurodegenerative diseases. In this paper, we introduce a novel graph regression model (GRM) for learning structural brain connectivity of Alzheimer’s disease (AD) measured by amyloid-β  deposits. The proposed GRM regards 11 C-labeled Pittsburgh Compound-B (PiB) positron emission tomography (PET) imaging data as smooth signals defined on an unknown graph. This graph is then estimated through an optimization framework, which fits the graph to the data with an adjustable level of uniformity of the connection weights. Under the assumed data model, results based on simulated data illustrate that our approach can accurately reconstruct the underlying network, often with better reconstruction than those obtained by both sample correlation and ℓ1 -regularized partial correlation estimation. Evaluations performed upon PiB-PET imaging data of 30 AD and 40 elderly normal control (NC) subjects demonstrate that the connectivity patterns revealed by the GRM are easy to interpret and consistent with known pathology. Moreover, the hubs of the reconstructed networks match the cortical hubs given by functional MRI. The discriminative network features including both global connectivity measurements and degree statistics of specific nodes discovered from the AD and NC amyloid-beta networks provide new potential biomarkers for preclinical and clinical AD.